TY  - CONF
AU  - Wisch, Julie K
AU  - McKay, Nicole S
AU  - Zammit, Matthew D
AU  - Christian, Bradley T
AU  - Schultz, Stephanie A
AU  - Millar, Peter R
AU  - Barthélemy, Nicolas R
AU  - Ryan, Natalie S
AU  - Renton, Alan E
AU  - Vermunt, Lisa
AU  - Joseph-Mathurin, Nelly
AU  - Shirzadi, Zahra
AU  - Strain, Jeremy F
AU  - Chrem, Patricio
AU  - Daniels, Alisha
AU  - Chhatwal, Jasmeer P
AU  - Cruchaga, Carlos
AU  - Ibanez, Laura
AU  - Jucker, Mathias
AU  - Day, Gregory S
AU  - Lee, Jae-Hong
AU  - Levin, Johannes
AU  - Llibre-Guerra, Jorge J
AU  - Aguillon, David
AU  - Roh, Jee Hoon
AU  - Supnet-Bell, Charlene
AU  - Xiong, Chengjie
AU  - Schindler, Suzanne E
AU  - Wang, Guoqiao
AU  - Li, Yan
AU  - Koeppe, Robert
AU  - Jack, Clifford R
AU  - Morris, John C
AU  - McDade, Eric
AU  - Bateman, Randall J
AU  - Benzinger, Tammie L S
AU  - Ances, Beau
AU  - Betthauser, Tobey J
AU  - Gordon, Brian A
TI  - Validation of Amyloid Chronicity in Autosomal Dominant Alzheimer Disease
JO  - Alzheimer's and dementia
VL  - 21
IS  - Suppl 2
SN  - 1552-5260
M1  - DZNE-2025-01463
SP  - e103008
PY  - 2025
AB  - Alzheimer Disease (AD) pathology evolves over decades, and understanding this progression is critical to the understanding of the disease and timing therapeutic interventions. Since individuals with Autosomal Dominant AD (ADAD) develop symptoms around the same age as their parent, it is possible to predict symptom onset and stage individuals by their estimated years to symptom onset (EYO). This approach does not generalize to other forms of AD, thus there is a pressing need for the timecourse of ADAD to be defined in broadly relevant terms. The objective of this project is to validate the Sampled Iterative Local Approximation (SILA) algorithm in a cohort with a known disease timecourse. SILA generates an estimate of time from amyloid positivity (Atime) based on longitudinal PET data.We evaluated Atime in a longitudinal ADAD sample (N = 316) with PET PiB data in three ways. First, we compared predicted age at amyloid positive (A+) to observed age at A+ for individuals who became A+ during enrollment. Next, using linear regression, we compared estimated age at A+ to estimated age at symptom onset (EYO=0). Finally, we used generalized additive models to compare the amount of variance in concurrent cognitive performance explained both Atime and EYO.We observed a mean average error of 1.15 years between actual age at A+ (N = 26) and the SILA-predicted Atime. Across all participants, SILA-estimated age at A+ explained 39
T2  - Alzheimer’s Association International Conference
CY  - 27 Jul 2025 - 31 Jul 2025, Toronto (Canada)
Y2  - 27 Jul 2025 - 31 Jul 2025
M2  - Toronto, Canada
KW  - Humans
KW  - Alzheimer Disease: diagnostic imaging
KW  - Alzheimer Disease: diagnosis
KW  - Alzheimer Disease: metabolism
KW  - Positron-Emission Tomography
KW  - Male
KW  - Female
KW  - Biomarkers: metabolism
KW  - Longitudinal Studies
KW  - Aged
KW  - Disease Progression
KW  - Middle Aged
KW  - Algorithms
KW  - Amyloid beta-Peptides: metabolism
KW  - Brain: diagnostic imaging
KW  - Brain: metabolism
KW  - Biomarkers (NLM Chemicals)
KW  - Amyloid beta-Peptides (NLM Chemicals)
LB  - PUB:(DE-HGF)1 ; PUB:(DE-HGF)16
C6  - pmid:41451762
C2  - pmc:PMC12741810
DO  - DOI:10.1002/alz70856_103008
UR  - https://pub.dzne.de/record/283056
ER  -